Loss Function
A function that measures how far model predictions are from actual values.
In-depth explanation
Loss functions quantify prediction error, guiding the optimization process. Different tasks use different loss functions: mean squared error (MSE) for regression, cross-entropy for classification, and specialized losses for ranking or detection. The choice of loss function significantly impacts what the model learns to optimize.
Examples
Related terms
More in Machine Learning
Classification
Predicting which category or class an input belongs to from a set of predefined categories.
Cross-Validation
A technique to evaluate model performance by training and testing on different subsets of data.
Ensemble Learning
Combining multiple models to produce better predictions than any single model.
Feature
An individual measurable property or characteristic of data used as input to a machine learning model.
Feature Engineering
The process of using domain knowledge to create new features that improve model performance.
Gradient Descent
An optimization algorithm that iteratively adjusts model parameters to minimize the loss function.
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